This important insight underlies the margins theory [1]}, introduced specifically to address the apparent lack of over-fitting in boosting.
The idea of data-dependent margin bounds has led to a line of increasingly sophisticated mathematical work on understanding data-dependent function space complexity with notions such as Rademacher Complexity [2]}. Yet, we note that as an explanation for the effectiveness of Adaboost, the margins theory had not been universally accepted (see, e.g., [3]} for an interesting discussion).
